Advances in Wireless and Mobile Networking

Topic Information

Dear Colleagues,

This Topic collects Selected Papers from the 15th edition of the IFIP Wireless and Mobile Networking Conference (IFIP WMNC 2024, https://www.unive.it/web/en/6439/home), which will be held in the wonderful city of Venice, Italy, on 11th-12th November 2024. Authors of papers presented at the conference are invited to submit extended versions of their work to this Topic for publication. IFIP WMNC 2024 will represent a discussion forum for researchers, professionals, and students interested in novel developments in mobile and wireless networks, services, and applications and mobile computing. IFIP WMNC combines PWC (Personal Wireless Communications conference), MWCN (Mobile and Wireless Communication Networks conference), and WSAN (Wireless Sensors and Actor Networks conference) into one event. Submissions outside this conference are welcomed as well. The main topics of interest are (but not limited to):

• Wireless and mobile communications and networks;
• Wireless technologies design and evaluation;
• Handoff, location, and resource management;
• Network and service management and control;
• Cognitive radio and massive MIMO networking;
• Network security solutions and protocols;
• Traffic and network modelling;
• Software-defined wireless networking;
• Wireless sensor and actuator networks;
• Low-power wide area networks;
• Vehicular cyber–physical networks;
• Communication technologies for the IoT;
• Ad hoc networking, positioning localization and tracking;
• Mobile cloud computing and applications;
• Multicasting and broadcasting issues;
• Intelligent transportation systems;
• Content management and distribution;
• ML/AI for network management;
• Knowledge, information, sensor, and data fusion: applications, approaches and algorithms;
• Unmanned aircraft systems/unmanned aerial vehicles/drones/remotely piloted aircraft systems (UAS/UAV/RPAS) wireless and mobile networks communications and applications.

Dr. Peppino Fazio
Dr. Eirini Eleni Tsiropoulou
Prof. Dr. Carlos Tavares Calafate
Dr. Danilo Amendola
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Drones drones	4.4	5.6	2017	19.2 Days	CHF 2600
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Future Internet futureinternet	2.8	7.1	2009	16.9 Days	CHF 1600
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Telecom telecom	2.1	4.8	2020	20.5 Days	CHF 1200

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Published Papers (9 papers)

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All Drones Electronics Future Internet Sensors Telecom

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26 pages, 2493 KiB  

Open AccessArticle

Resource Allocation and Interference Coordination Strategies in Heterogeneous Dual-Layer Satellite Networks

by Jinhong Li, Rong Chai, Tianyi Zhou and Chengchao Liang

Sensors 2025, 25(4), 1005; https://doi.org/10.3390/s25041005 - 8 Feb 2025

Viewed by 646

Abstract

In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge [...] Read more.

In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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22 pages, 2471 KiB  

Open AccessArticle

Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning

by Jinfang Jiang, Yiling Dong, Guangjie Han and Gang Su

Drones 2025, 9(2), 123; https://doi.org/10.3390/drones9020123 - 7 Feb 2025

Cited by 1 | Viewed by 535

Abstract

In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, [...] Read more.

In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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37 pages, 1824 KiB  

Open AccessArticle

Carrier Frequency Offset Impact on Universal Filtered Multicarrier/Non-Uniform Constellations Performance: A Digital Video Broadcasting—Terrestrial, Second Generation Case Study

by Sonia Zannou, Anne-Carole Honfoga, Michel Dossou and Véronique Moeyaert

Telecom 2024, 5(4), 1205-1241; https://doi.org/10.3390/telecom5040061 - 4 Dec 2024

Viewed by 860

Abstract

Digital terrestrial television is now implemented in many countries worldwide and is now mature. Digital Video Broadcasting-Terrestrial, second generation (DVB-T2) is the European standard adopted or deployed by European and African countries which uses Orthogonal Frequency-Division Multiplexing (OFDM) modulation to achieve good throughput [...] Read more.

Digital terrestrial television is now implemented in many countries worldwide and is now mature. Digital Video Broadcasting-Terrestrial, second generation (DVB-T2) is the European standard adopted or deployed by European and African countries which uses Orthogonal Frequency-Division Multiplexing (OFDM) modulation to achieve good throughput performance. However, its main particularity is the number of subcarriers operated for OFDM modulation which varies from 1024 to 32,768 subcarriers. Also, mobile reception is planned in DVB-T2 in addition to rooftop antenna and portable receptions planned in DVB-T. However, the main challenge of DVB-T2 for mobile reception is the presence of a carrier frequency offset (CFO) which degrades the system performance by inducing an Intercarrier Interference (ICI) on the DVB-T2 signal. This paper evaluates the system performance in the presence of the CFO when Gaussian noise and a TU6 channel are applied. Universal Filtered Multicarrier (UFMC) and non-uniform constellations (NUCs) have previously demonstrated good performance in comparison with OFDM and Quadrature Amplitude Modulation (QAM) in DVB-T2. The impact of CFO on the UFMC- and NUC-based DVB-T2 system is additionally investigated in this work. The results demonstrate that the penalties induced by CFO insertion in UFMC- and NUC-based DVB-T2 are highly reduced in comparison to those for the native DVB-T2. At a bit error rate (BER) of ${10}^{-3}$, the CFO penalties induced by the native DVB-T2 are $0.96$$\mathrm{d}$$\mathrm{B}$ and 4 $\mathrm{d}$$\mathrm{B}$, respectively, when only Additive White Gaussian Noise (AWGN) is used and when TU6 is additionally considered. The penalties are equal to $0.84$$\mathrm{d}$$\mathrm{B}$ and $0.2$$\mathrm{d}$$\mathrm{B}$ for UFMC/NUC-based DVB-T2. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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27 pages, 9297 KiB  

Open AccessArticle

Integrating Connected Vehicles into IoT Ecosystems: A Comparative Study of Low-Power, Long-Range Communication Technologies

by Valentin Iordache, Marius Minea, Răzvan Andrei Gheorghiu, Florin Bădău, Angel Ciprian Cormoș, Valentin Alexandru Stan, Ion Nicolae Stăncel and Victor Stoica

Sensors 2024, 24(23), 7607; https://doi.org/10.3390/s24237607 - 28 Nov 2024

Viewed by 1333

Abstract

Integrating road vehicles into broader Internet of Things (IoT) ecosystems is an important step in the development of fully connected and smart transportation systems. This research explores the potential of using communication technologies that achieve a balance between low-power and long-range (LPLR) capabilities [...] Read more.

Integrating road vehicles into broader Internet of Things (IoT) ecosystems is an important step in the development of fully connected and smart transportation systems. This research explores the potential of using communication technologies that achieve a balance between low-power and long-range (LPLR) capabilities while remaining cost-effective, specifically Bluetooth Classic BR-EDR, Bluetooth LE, ZigBee, nRF24, and LoRa—for Vehicle-to-Infrastructure (V2I) and Vehicle-to-IoT (V2IoT) ecosystem interactions. During this research, several field tests were conducted employing different types of communication modules, across three distinct environments: an open-field inter-urban road, a forest inter-urban road, and an urban road. The modules were evaluated based on the communication range, messaging rate, error rate, and geographical data from GNSS (Global Navigation Satellite System) coordinates, using point-to-point communication between a roadside unit (RSU) and a moving vehicle equipped with an onboard unit (OBU). The results demonstrate the usability of these technologies for integrating vehicles into both public infrastructure (for V2I services) and private IoT systems, highlighting their potential for scalable, cost-effective deployment in smart transportation systems. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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21 pages, 6739 KiB  

Open AccessArticle

A Novel Energy Replenishment Algorithm to Increase the Network Performance of Rechargeable Wireless Sensor Networks

by Tariq, Vishwanath Eswarakrishnan, Adil Hussain, Zhu Wei and Muhammad Uzair

Sensors 2024, 24(23), 7491; https://doi.org/10.3390/s24237491 - 24 Nov 2024

Viewed by 877

Abstract

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to [...] Read more.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to transmit wireless energy to the sensor node to improve the network’s throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by the joint scheduling of an MC and a DC. We proved we could transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selected a total horizon of 360° and then selected the horizon of each specific ${90}^{\circ }$ area based on its antenna orientation. The DC’s orientation was scheduled for each time slot. Furthermore, multiple MCs were used to transmit energy for sensor nodes that could not be covered by the DC. We divided the rechargeable wireless sensor network into several zones via a Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtained the optimal charging locations of the MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research was to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network could increase, and the end delay could be maximized, with joint energy transmission. Extensive simulation results demonstrated that our RWSNs were designed to significantly improve network lifetime over the baseline method. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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24 pages, 2020 KiB  

Open AccessArticle

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

by Abbas Kubba, Hafedh Trabelsi and Faouzi Derbel

Future Internet 2024, 16(11), 425; https://doi.org/10.3390/fi16110425 - 17 Nov 2024

Viewed by 4902

Abstract

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and [...] Read more.

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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26 pages, 4934 KiB  

Open AccessArticle

Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure

by Naba Raj Khatiwoda, Babu Ram Dawadi and Sashidhar Ram Joshi

Future Internet 2024, 16(11), 423; https://doi.org/10.3390/fi16110423 - 14 Nov 2024

Cited by 1 | Viewed by 2658

Abstract

With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper [...] Read more.

With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper planning procedures are to be adopted to provide cost-effective and quality telecommunication services. In this paper, we planned 5G network deployment in two frequency ranges, 3.5 GHz and 28 GHz, using a mixed cell structure. We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. The comparative analysis of metaheuristic algorithms shows that the proposed network is efficient in providing an average data rate of 50 Mbps, can meet the coverage requirements of at least 98%, and meets quality-of-service requirements. We carried out the case study for an urban area and another suburban area of Kathmandu Valley, Nepal. We analyzed the outcomes of 5G greenfield deployment and 5G deployment using existing 4G infrastructure. Deploying 5G networks using existing 4G infrastructure, resources can be saved up to 33.7% and 54.2% in urban and suburban areas, respectively. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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16 pages, 308 KiB  

Open AccessArticle

Enhancing TCP Airtime Fairness through Precise Computation for Upload and Download Flows in WiFi Networks

by Yuhao Chen and Jinyao Yan

Telecom 2024, 5(4), 992-1007; https://doi.org/10.3390/telecom5040050 - 2 Oct 2024

Viewed by 1309

Abstract

Airtime fairness has emerged as a key approach to enhancing wireless throughput performance. However, existing research often overlooks the precise calculation of airtime, particularly in relation to TCP acknowledgments. This paper introduces a novel method, implemented on the access point side, for accurately [...] Read more.

Airtime fairness has emerged as a key approach to enhancing wireless throughput performance. However, existing research often overlooks the precise calculation of airtime, particularly in relation to TCP acknowledgments. This paper introduces a novel method, implemented on the access point side, for accurately calculating the airtime of TCP and UDP flows. Building on this, we propose a QoS-based scheduling algorithm designed to improve fairness between upload and download traffic. The effectiveness of the algorithm is validated through experiments that accurately measure both throughput and airtime for upload and download traffic. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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21 pages, 5536 KiB  

Open AccessArticle

A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models

by Ilia Iliev, Yuliyan Velchev, Peter Z. Petkov, Boncho Bonev, Georgi Iliev and Ivaylo Nachev

Sensors 2024, 24(17), 5855; https://doi.org/10.3390/s24175855 - 9 Sep 2024

Viewed by 2394

Abstract

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning [...] Read more.

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as $7.3$ dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 $\mathrm{M}$$\mathrm{Hz}$, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)—LoRa, etc. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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